Feature Pyramid biLSTM: Using Smartphone Sensors for Transportation Mode Detection

• URL: http://arxiv.org/abs/2310.11087v1
• Date: Tue, 17 Oct 2023 09:13:10 GMT
• Title: Feature Pyramid biLSTM: Using Smartphone Sensors for Transportation Mode Detection
• Authors: Qinrui Tang, Hao Cheng
• Abstract summary: We propose a novel end-to-end approach to explore a reduced amount of sensory data collected from a smartphone. Our approach, called Feature Pyramid biLSTM (FPbiLSTM), is characterized by its ability to reduce the number of sensors required and processing demands. FPbiLSTM extends an existing CNN biLSTM model with the Feature Pyramid Network, leveraging the advantages of both shallow layer richness and deeper layer feature resilience.
• Score: 5.182070755214674
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The widespread utilization of smartphones has provided extensive availability to Inertial Measurement Units, providing a wide range of sensory data that can be advantageous for the detection of transportation modes. The objective of this study is to propose a novel end-to-end approach to effectively explore a reduced amount of sensory data collected from a smartphone to achieve accurate mode detection in common daily traveling activities. Our approach, called Feature Pyramid biLSTM (FPbiLSTM), is characterized by its ability to reduce the number of sensors required and processing demands, resulting in a more efficient modeling process without sacrificing the quality of the outcomes than the other current models. FPbiLSTM extends an existing CNN biLSTM model with the Feature Pyramid Network, leveraging the advantages of both shallow layer richness and deeper layer feature resilience for capturing temporal moving patterns in various transportation modes. It exhibits an excellent performance by employing the data collected from only three out of seven sensors, i.e. accelerometers, gyroscopes, and magnetometers, in the 2018 Sussex-Huawei Locomotion (SHL) challenge dataset, attaining a noteworthy accuracy of 95.1% and an F1-score of 94.7% in detecting eight different transportation modes.

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